Network Capacity Planning for QoE Threshold Prevention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Networks face quality of experience (QoE) issues such as delay and service failure when they reach or exceed their maximum capacity, leading to suboptimal user experience.
Innovation Solution
A system that analyzes network capacity, predicts potential QoE issues, and implements solutions like adding additional channels to prevent threshold exceedance, using data collection devices, analysis centers, and communication interfaces to monitor and manage network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network capacity is increased to prevent QoE issues, then user experience is improved, but network cost and complexity increase
Solution Approach 1:
The system performs preliminary capacity planning by analyzing historical network data, traffic patterns, and growth trends to predict future capacity requirements before QoE degradation occurs. This allows proactive network expansion planning rather than reactive responses to service failures.
Solution Approach 2:
The system continuously monitors network performance metrics, user experience quality, and capacity utilization levels, then feeds this information back to automatically adjust capacity planning recommendations. This closed-loop feedback mechanism optimizes network capacity dynamically based on actual usage patterns.
2Measurement precision
If network monitoring and analysis systems are deployed to predict QoE issues, then network performance is improved, but system complexity increases
Solution Approach 1:
The network system performs self-diagnosis and self-planning by automatically collecting performance data, analyzing capacity trends, and generating expansion recommendations without requiring external intervention. The system serves its own capacity planning needs through embedded analytics.
Solution Approach 2:
The capacity planning system is designed to handle multiple network types, traffic patterns, and performance metrics through a unified analysis framework. This multi-functional approach reduces overall system complexity by avoiding specialized subsystems for different scenarios.
Data Source
AI summary
A network device is configured to receive information relating to factors associated with quality of experience issues. The network device is configured to analyze the information. The network device is configured to predict that a quality of experience factor associated with a particular type of communication will exceed a threshold level a future time. The network device is configured to send a message to the device, the device generating a rule or policy; and the rule or policy instructing one or more other network devices to increase a capacity of the network to prevent the quality of experience factor from exceeding the threshold value at the future time.